The Dangers of AI Nationalism and Beggar-Thy-Neighbour Policies

As they attempt to nurture and govern AI, some nations are acting in ways that – with or without direct intent – discriminate among foreign market actors. For example, some governments are excluding foreign firms from access to incentives for high-speed computing, or requiring local content in the AI supply chain, or adopting export controls for the advanced chips that power many types of AI. If policy makers in country X can limit access to the building blocks of AI – whether funds, data or high-speed computing power – it might slow down or limit the AI prowess of its competitors in country Y and/or Z. At the same time, however, such policies could violate international trade norms of non-discrimination. Moreover, if policy makers can shape regulations in ways that benefit local AI competitors, they may also impede the competitiveness of other nations’ AI developers. Such regulatory policies could be discriminatory and breach international trade rules as well as long-standing rules about how nations and firms compete – which, over time, could reduce trust among nations. In this article, the author attempts to illuminate AI nationalism and its consequences by answering four questions:

– What are nations doing to nurture AI capacity within their borders?

– Are some of these actions trade distorting?

 – Are some nations adopting twenty-first century beggar thy neighbour policies?

– What are the implications of such trade-distorting actions?

The author finds that AI nationalist policies appear to help countries with the largest and most established technology firms across multiple levels of the AI value chain. Hence, policy makers’ efforts to dominate these sectors, as example through large investment sums or beggar thy neighbour policies are not a good way to build trust.

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Talking to a Brick Wall: The US Government’s Response to Public Comments on AI

April 28, 2025

Building trust in artificial intelligence (AI) is an elusive goal, especially if AI models are closed or partially open, making it difficult for users to determine if these models are reliable, fair or trustworthy. For this reason, the Biden administration sought public input on the potential risks and benefits of these models as well as policy approaches. In an executive order, he tasked the assistant secretary of commerce for communications and information (who was also head of the National Telecommunications and Information Agency [NTIA]) to solicit feedback through a public consultation process. NTIA advises the president on information, telecommunications and related technology policy, including AI. The author used a landscape analysis to examine the dialogue between US officials and the public response. Although some 300 Americans participated in the dialogue, these commenters did not provide a representative sample of Americans who use or might be affected by open versus closed AI systems. Those who did provide their opinions likely had a direct stake in these issues. The dialogue was also dysfunctional because policy makers did not really listen to — or even report on — what they heard.

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Trump 2.0: Clash of the tech bros

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Getty Images

In 2016, tariff man couldn’t care less about tech. Newly elected U.S. President Donald J. Trump knew that the people who created and ran America’s tech giants were richer and smarter than him. Moreover, they had different values. They embraced openness, accountability, and the rule of law in international affairs. Moreover, they generally advocated for increased immigration of skilled workers, open markets, and relatively unfettered cross-border data flows. Unsurprisingly, his first term was characterized by disputes with many tech giants, including Facebook and Amazon. However, tech leaders and government officials had a common concern: China’s innovative tech firms.

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The Age of AI Nationalism and Its Effects

September 30, 2024

Policy makers in many countries are determined to develop artificial intelligence (AI) within their borders because they view AI as essential to both national security and economic growth. Some countries have proposed adopting AI sovereignty, where the nation develops AI for its people, by its people and within its borders. In this paper, the author makes a distinction between policies designed to advance domestic AI and policies that, with or without direct intent, hamper the production or trade of foreign-produced AI (known as “AI nationalism”). AI nationalist policies in one country can make it harder for firms in another country to develop AI. If officials can limit access to key components of the AI supply chain, such as data, capital, expertise or computing power, they may be able to limit the AI prowess of competitors in country Y and/or Z. Moreover, if policy makers can shape regulations in ways that benefit local AI competitors, they may also impede the competitiveness of other nations’ AI developers. AI nationalism may seem appropriate given the import of AI, but this paper aims to illuminate how AI nationalistic policies may backfire and could divide the world into AI haves and have nots.

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AI could become the ‘new steel’ as overcapacity risk goes unnoticed

July 24, 2024
Policymakers in the U.S., Saudi Arabia, Japan, the U.K., and the EU have announced huge public investments in artificial intelligence, which follow large private sector investments.
Hu Guan – Xinhua – Getty Images

In the 19th century, government officials came to understand that steel would be essential to both economic growth and national security. Thus, they devised policies that could sustain local production as well as prevent foreign producers from competing in domestic markets.

While steel and AI could not be more different, many economists view AI as a general-purpose technology that can stimulate both economic growth and innovation. Hence, policymakers must ensure domestic capacity.

However, many government officials also already see AI as a critical technology essential to both national security and economic progress.

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Data Disquiet: Concerns about the Governance of Data for Generative AI

The growing popularity of large language models (LLMs) has raised concerns about their accuracy. These chatbots can be used to provide information, but it may be tainted by errors or made-up or false information (hallucinations) caused by problematic data sets or incorrect assumptions made by the model. The questionable results produced by chatbots has led to growing disquiet among users, developers and policy makers. The author argues that policy makers need to develop a systemic approach to address these concerns. The current piecemeal approach does not reflect the complexity of LLMs or the magnitude of the data upon which they are based, therefore, the author recommends incentivizing greater transparency and accountability around data-set development.

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Facing Reality: Canada Needs to Think about Extended Reality and AI

Although Canada is a leader in becoming the first nation to develop an artificial intelligence (AI) strategy, it is falling behind other countries in extended reality (XR) competitiveness. In this paper, the authors look at why Canada is lagging in this area and what can be done to bring the country up to speed with its peers. The authors argue that more attention and funding should be directed toward the development of XR technology in Canada because XR is already a major contributor to the Canadian and global economy; XR and AI will shape future iterations of the internet; a variant of XR (digital twins, which serve as models of people or objects) can serve as tools to develop mitigating strategies for various types of complex problems; and other nations, such as China and South Korea, are investing heavily in XR technology to gain a competitive edge.

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How to Regulate AI? Start With the Data

We live in an era of data dichotomy. On one hand, AI developers rely on large data sets to “train” their systems about the world and respond to user questions. These data troves have become increasingly valuable and visible. On the other hand, despite the import of data, U.S. policy makers don’t view data governance as a vehicle to regulate AI.

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Building Trust in AI: A Landscape Analysis of Government AI Programs

Building Trust in AI: A Landscape Analysis of Government AI Programs

As countries around the world expand their use of artificial intelligence (AI), the Organisation for Economic Co-operation and Development (OECD) has developed the most comprehensive website on AI policy, the OECD.AI Policy Observatory.

Although the website covers public policies on AI, Aaronson found that many governments failed to evaluate or report on their AI initiatives. This lack of reporting is a missed opportunity for policy makers to learn from their programs (the author found that less than one percent of the programs listed on the OECD.AI website had been evaluated).

In addition, Aaronson found discrepancies between what governments said they were doing on the OECD.AI website and what they reported on their own websites. In some cases, there was no evidence of government actions; in other cases, links to government sites did not work. Evaluations of AI policies are important because they help governments demonstrate how they are building trust in both AI and AI governance and that policy makers are accountable to their fellow citizens.

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America’s uneven approach to AI and its consequences

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The ecological balance of the ocean has been disturbed by invasive species and cholera. Many pesticides and nutrients used in agriculture end up in the coastal waters, resulting in oxygen depletion that kills marine plants and shellfish. Meanwhile the supply of fish is declining due to overfishing. Yet to flourish, humankind requires healthy oceans; the oceans generate half of the oxygen we breathe, and, at any given moment, they contain more than 97% of the world’s water. Oceans provide at least a sixth of the animal protein people eat. Living oceans absorb carbon dioxide from the atmosphere and reduce climate change impacts.

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